Dynamic Multi-population Artificial Bee Colony Algorithm

Abstract

As a relatively new paradigm of bio-inspired optimization techniques, artificial bee colony (ABC) algorithm has attracted much attention for its simplicity and effectiveness. However, the performance of ABC is not satisfactory when solving some complex optimization problems. To improve its performance, we propose a novel ABC variant by designing a dynamic multi-population scheme (DMPS). In DMPS, the population is divided into several subpopulations, and the size of subpopulation is adjusted dynamically by checking the quality of the global best solution. Moreover, we design two novel solution search equations to maximize the effectiveness of DMPS, in which the local best solution of each subpopulation and the global best solution of the whole population are utilized simultaneously. In the experiments, 32 widely used benchmark functions are used, and four well-established ABC variants are involved in the comparison. The comparative results show that our approach performs better on the majority of benchmark functions.